Triple
T33653063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lufthansa First Class product |
E862149
|
entity |
| Predicate | inFlightFeature |
P39033
|
FINISHED |
| Object | multi-course restaurant-style meals |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: multi-course restaurant-style meals | Statement: [Lufthansa First Class product, inFlightFeature, multi-course restaurant-style meals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inFlightFeature Context triple: [Lufthansa First Class product, inFlightFeature, multi-course restaurant-style meals]
-
A.
inFlightService
chosen
Indicates that a service or activity is being provided to passengers during a flight.
-
B.
hasFlightCharacteristic
Indicates that an entity possesses a specific property, quality, or behavior related to flight.
-
C.
flightDeckFeature
Indicates that one entity is a feature, component, or element that is part of or present on the flight deck of another entity.
-
D.
testFlight
Indicates that an aircraft or spacecraft is being flown under controlled conditions to evaluate its performance, safety, or functionality before regular use.
-
E.
offersInFlightEntertainment
Indicates that a service or provider makes entertainment options available to passengers during a flight.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f349840ba881908e3bfce536aeb92b |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6fb19063c81909466b329655c8583 |
completed | May 3, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f6f96badb08190994442c2aba840b1 |
completed | May 3, 2026, 7:29 a.m. |
Created at: May 1, 2026, 1:42 a.m.